Wan Prompt Generator

enerating video with WAN is not hard. Generating video that looks like what you intended is where most people get stuck.

The gap is almost always the prompt. Not because prompting is complicated, but because WAN’s diffusion architecture has specific expectations about how information should be sequenced, and when those expectations are not met, the model fills in the blanks on its own terms. Subject drift, flickering fabric, hands that deform mid-clip, cameras that wander when you wanted them locked: these are not random failures. They are predictable outputs from prompts that left too much undefined.

Feedough’s WAN prompt generator is built around how WAN actually parses instructions. It takes five inputs, sequences them in the priority order the model’s attention mechanism weights, writes the motion description at the level of specificity the model needs, includes negative prompt construction for the artifact patterns most likely in your specific scene type, and produces output ready to paste directly into any WAN interface. You get a production-grade prompt in two minutes instead of thirty.

What Is a WAN Prompt Generator?

WAN is an AI video generation model developed by Alibaba’s Tongyi Laboratory. The WAN 2.1 and 2.2 versions are open source. It runs on a diffusion-based architecture that generates video frames by progressively denoising a latent representation, guided by your text prompt. The model has shipped through several versions, each improving motion realism, subject consistency, and the accuracy of camera behavior reproduction.

What makes WAN different from image generators is the temporal dimension. The model is not producing a single frame. It is maintaining subject identity, motion continuity, lighting consistency, and camera behavior across every frame of the clip simultaneously. That is a harder task, and it requires a more structured prompt to manage well.

WAN parses your prompt in a strict priority hierarchy: subject identity first, motion and action second, camera behavior third, style and atmosphere fourth. This is not a convention. It reflects how the diffusion model weights attention across prompt tokens during the generation process. Prompts that open with mood or atmosphere and bury the subject description in the middle produce outputs where the model anchors composition around the wrong element. Prompts that front-load a complete subject description produce consistent, on-model outputs because the model has a stable identity anchor before it begins resolving motion and style.

A WAN prompt generator is a tool that structures your creative inputs into a prompt that respects this hierarchy. It handles the five components every professional WAN prompt requires: subject identity block, motion description, camera instruction, environment and lighting, and visual style, plus the negative prompt that suppresses the artifact patterns most likely to appear in your specific scene.

Feedough’s WAN prompt generator does this through five structured inputs. You describe the scene in plain language across each input. The generator produces a complete, correctly sequenced prompt with both positive and negative components.

How to Create a WAN Prompt Using Feedough’s WAN Prompt Generator

Each input targets a specific layer of how WAN interprets the final prompt. Fill all five with precision and the generator produces output that gives the model no ambiguity to fill with its own defaults.

Step 1: Describe Your Primary Subject and Visual Characteristics

This textarea is the subject identity block, and it is the most important input in the entire generator. WAN anchors its entire composition, framing, and motion interpretation around the first clear subject identity it encounters in the prompt. Whatever you define here, the model treats as the fixed reference point across every frame.

The most common failure mode in WAN generation is subject drift: the model’s internal representation of the subject shifts between frames, causing faces to morph, proportions to change, and clothing to alter mid-clip. The fix is completeness here. Include every physical attribute that defines recognizability across frames: hair length, color, and texture; skin tone; face structure; clothing color, material, and fit; build; posture; any distinctive accessories. Then state the subject’s position or stance in the opening frame.

Weak: “A woman in a green sweater standing in a room.”

Strong: “A woman in her late twenties, shoulder-length auburn hair, pale skin with freckles, wearing an oversized dark green knit sweater and light-wash jeans, standing barefoot on a hardwood floor. Slim build, relaxed posture, looking slightly downward.”

The difference is not stylistic preference. It is the amount of constraint you give the model. More constraint means less invention, which means more stability across frames.

Step 2: Describe the Action and Motion in the Video

This textarea covers everything that moves: the primary subject’s action, secondary environmental motion like hair, fabric, foliage, or steam, and any object interactions. This is where most WAN prompts fail. Vague motion language gives the diffusion model degrees of freedom it fills with its training distribution defaults, which are rarely what you want.

Every moving element needs four things specified: what is moving, in which direction, at what pace, and with what physical quality. “Hair sways” is insufficient. “Long auburn hair moves in a slow left-to-right indoor breeze, individual strands lifting slightly at the ends” is a motion description. “She raises a mug” is insufficient. “She slowly raises a ceramic mug to her lips with both hands, steam rising from the mug in a slow upward drift” is a motion description.

Include secondary motion explicitly. Fabric behavior, steam, floating particles, window light shifting slightly, foliage moving in background, these details matter because WAN will generate secondary motion regardless of whether you specify it. If you do not, the model invents behavior from its training distribution. Fabric flicker and texture banding are two of the most common WAN artifacts, and they are mostly avoidable when fabric motion is described rather than left unspecified.

Step 3: Select Your Camera Behavior and Shot Type

This is a dropdown with eleven options: static locked, slow dolly-in, slow dolly-out, slow horizontal pan, slow orbital arc, crane up, low-angle wide, tracking shot, aerial top-down slow drift, subtle handheld micro-movement, and close-up macro static. An other option is available for shot types not listed.

Camera instruction is not optional for professional-quality output. WAN has a strong prior toward whatever camera behavior its training distribution contains most for a given scene type. For a person standing in a room, the prior is a static shot. For a car on a road, the prior is a tracking shot. If you want anything other than the model’s default for your scene type, you must state it explicitly using cinematography language. WAN understands these terms and responds to them correctly when they are present.

Every camera selection gets translated in the generated prompt with a pace modifier and a stability instruction. “Slow dolly-in” becomes “slow dolly-in toward the subject, smooth and stable, no jitter.” This pairing is important because WAN can produce the correct camera movement type with shaky or inconsistent execution when stability is not specified.

Step 4: Describe the Scene Environment and Lighting

This textarea covers location type, time of day, atmospheric conditions, and the lighting setup. Lighting is the detail most people under-specify, and it is the single element that most separates amateur from professional WAN outputs.

Name the light source, its position relative to the subject, its quality, and its color temperature. “Soft natural light coming through large windows on the left, diffused through white linen curtains” is a lighting description. “Good lighting” is not. Include fill light sources if they exist. “Warm interior ambient light from a floor lamp to the right” adds a second light source that shapes the scene’s color balance and prevents the model from inventing its own ambient interpretation.

Background treatment also belongs here. Name what is visible in the background and how it is rendered. “Bookshelves and a plant visible but soft-focused in the background” tells the model the depth of field and prevents background detail from competing with subject focus.

Step 5: Select Your Visual Style and Mood

This dropdown has eight options: cinematic film look with warm color grade and 24fps grain, photorealistic documentary with natural ungraded tones, commercial lifestyle with bright and clean polish, moody atmospheric with cool shadows and high contrast, soft dreamy with diffused light and muted tones, vintage retro film with faded grain and nostalgic palette, dark dramatic with heavy shadows and intense contrast, and bright airy with high key and minimal shadows. An other option is available.

Style must be a single coherent aesthetic family. The dropdown options are grouped this way by design. Each selection gets translated in the generated prompt into the aesthetic family name plus two to three specific technical descriptors that reinforce it. “Cinematic film look” becomes “cinematic film look, warm amber color grade, 24fps motion cadence, shallow depth of field.” This specificity is what makes the style directive actionable for the model. “Cinematic” alone is too broad. The technical descriptors are what produce consistent results.

Do not attempt to mix aesthetic families. Photorealistic and soft dreamy are incompatible. Cinematic and vintage retro can coexist if handled carefully, but the generator is not designed for hybrid style inputs. If your intent does not match any of the eight options, use the other field and describe your aesthetic family with one name and two to three specific technical terms.

Step 6: Generate and Use Your Prompt

Click Generate. The output arrives in two clearly labeled parts: the main positive prompt as flowing prose between 80 and 200 words, and the negative prompt on a new line as a comma-separated list of 8 to 15 specific artifact terms targeted to your scene type.

The negative prompt is constructed based on which artifact patterns are most likely given the specific combination of inputs you provided. Close-up face work generates a negative prompt targeting face morphing, expression drift, and eye asymmetry. Any scene with hands gets deformed wrist and extra finger suppression added. Fabric-heavy scenes get texture flickering and color banding suppression. The generator does not use a generic negative prompt template. It builds the negative prompt for your scene.

Copy both sections and paste them into your WAN interface. On create.wan.video, use the text-to-video mode and paste the positive prompt into the main input. If the platform you are using has a separate negative prompt field, paste each section accordingly. If it uses a single input, include both sections with the “Negative prompt:” label intact.

If the output needs adjustment, return to the generator and refine the input that corresponds to the problem. Subject drift means the subject description in Step 1 needs more physical specificity. Incorrect motion means Step 2 needs more directional and pacing detail. Wrong camera behavior means Step 3 needs a different selection or a custom description. Style inconsistency means Step 5 needs a cleaner aesthetic family choice. Targeted input changes produce more reliable corrections than manually rewriting the generated prompt.

Why Use Feedough’s WAN Prompt Generator?

The gap between a mediocre WAN output and a usable one almost always comes down to three things: how completely the subject is defined, how specifically the motion is described, and whether the negative prompt targets the right artifact patterns. Most people writing WAN prompts from scratch get partial credit on the first, skip the second, and ignore the third entirely.

Writing motion descriptions at the required level of specificity takes time and practice. Most creators who use WAN regularly develop a mental template over dozens of iterations, learning through failure which vague language triggers which artifacts. The generator encodes that knowledge directly into the output. You get the benefit of that iteration without having to run it yourself.

The negative prompt construction is the part that is hardest to do well manually. Knowing which artifact patterns are most likely for a given scene type, and knowing the specific term strings that reliably suppress them, requires the kind of practical production experience that accumulates over hundreds of clips across many different scene types. The generator applies that knowledge automatically based on your inputs.

The correct priority sequencing of the prompt components is handled by default. Subject identity is always front-loaded. Motion is always described before camera. Camera is always described before style. You do not have to think about ordering. The generator produces a prompt that follows WAN’s attention weighting correctly every time.

For anyone producing video content at volume, for social media, commercial work, narrative projects, or product campaigns, this consistency matters. A prompt structure that produces reliable first-pass outputs is worth significantly more than one that occasionally produces a great clip after six or seven iterations.

Frequently Asked Questions